September 15, 2011

Identifying Potential Participants from an EMR, Part 2: Now That’s More Like It!

Last November I wrote (okay, whined) about the challenges to selecting a pool of potential subjects for the design phase of the BreathEasy project. At that time, the limited data in our newly implemented EMR made it necessary to use medication lists to find patients. It was both complicated (I needed an EMR data programmer to do the query) and messy (I did a lot of chart reviews to winnow a list of 271 down to 151).

What a difference a few months has made! We needed to identify a slightly different patient pool for the evaluation phase. In the interim, clinicians at our two study practices, including me, have been diligently inputting medical conditions into the EMR’s problem list. With that additional data, this time we simply looked for patients with asthma on the problem list, a prescription for an asthma controller medicine prescription in the last 12 months (a proxy for moderate to severe asthma) and residence in an urban zip code. A second advantage was that had I learned to use the EMR data query tool. As a result I could do the query myself, come up with a list of 83 patients that I was confident met our selection criteria, and avoid hours of chart reviews. After just a few days of calls, we've already recruited a quarter of the30 participants needed. If necessary, it will be simple for me to expand the pool a bit by replacing the maintenance medication criteria with a prescription for a steroid taper (used to treat asthma exacerbations, so another reasonable proxy for more than mild asthma).

This time it was actually fun. Bottom line: Better data plus a data retrieval tool I could use myself equals better results with a lot less effort.

Comments

Last November I wrote (okay, whined) about the challenges to selecting a pool of potential subjects for the design phase of the BreathEasy project. At that time, the limited data in our newly implemented EMR made it necessary to use medication lists to find patients. It was both complicated (I needed an EMR data programmer to do the query) and messy (I did a lot of chart reviews to winnow a list of 271 down to 151).

What a difference a few months has made! We needed to identify a slightly different patient pool for the evaluation phase. In the interim, clinicians at our two study practices, including me, have been diligently inputting medical conditions into the EMR’s problem list. With that additional data, this time we simply looked for patients with asthma on the problem list, a prescription for an asthma controller medicine prescription in the last 12 months (a proxy for moderate to severe asthma) and residence in an urban zip code. A second advantage was that had I learned to use the EMR data query tool. As a result I could do the query myself, come up with a list of 83 patients that I was confident met our selection criteria, and avoid hours of chart reviews. After just a few days of calls, we've already recruited a quarter of the30 participants needed. If necessary, it will be simple for me to expand the pool a bit by replacing the maintenance medication criteria with a prescription for a steroid taper (used to treat asthma exacerbations, so another reasonable proxy for more than mild asthma).

This time it was actually fun. Bottom line: Better data plus a data retrieval tool I could use myself equals better results with a lot less effort.